Skip to main content
Glama
ClintMoody

deep-think-mcp

by ClintMoody

compress_history

Creates a concise extractive summary of previous session stages, limited to a target token count, enabling small-context models to access history without exceeding context limits.

Instructions

Deterministic extractive digest of prior stages' committed thoughts, capped at target_tokens (a cheap len(text)//4 heuristic -- no tokenizer dependency). The current stage is left out; its detail is already visible via the live loop tools/ summarize_session. For small-context local models that can't hold a whole session's history.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
session_idYes
target_tokensNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description fully explains key behaviors: deterministic, extractive digest, capping via target_tokens using a cheap heuristic, and exclusion of the current stage. It does not mention side effects on session state, but the action is read-only in nature.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single dense paragraph but front-loads the core purpose. It is efficient but could be structured more clearly (e.g., separate sentences for usage context). No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity, the presence of an output schema, and sibling tools for context (e.g., `summarize_session`), the description provides adequate information. It could mention error conditions or empty history, but is otherwise complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description must compensate. It explains `target_tokens` (default 300, heuristic of len(text)//4) but does not clarify the meaning or purpose of `session_id`, which is the required parameter. Thus, partial compensation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool produces a 'deterministic extractive digest of prior stages' committed thoughts', distinguishing it from summarization of the current stage via `summarize_session`. The verb 'compress' and resource 'history' are specific, and the use case for small-context models is explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description indicates when to use the tool (for small-context local models that cannot hold full history) and mentions an alternative (`summarize_session` for the current stage). It does not explicitly state when not to use or list other alternatives among siblings, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ClintMoody/deep-think-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server